https://github.com/habedi/rag-playground

A playground for learning about retrieval augmented generation (RAG)
https://github.com/habedi/rag-playground

generative-ai llms nlp python rag retrieval-augmented-generation semantic-search vector-database

Last synced: 7 months ago
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A playground for learning about retrieval augmented generation (RAG)

• Host: GitHub
• URL: https://github.com/habedi/rag-playground
• Owner: habedi
• License: mit
• Created: 2024-04-12T15:30:20.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-18T10:44:57.000Z (8 months ago)
• Last Synced: 2025-02-18T11:36:06.618Z (8 months ago)
• Topics: generative-ai, llms, nlp, python, rag, retrieval-augmented-generation, semantic-search, vector-database
• Language: Jupyter Notebook
• Homepage:
• Size: 15.3 MB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# A Playground for RAG-related Stuff

[![Tests](https://github.com/habedi/rag-playground/actions/workflows/tests.yml/badge.svg)](https://github.com/habedi/rag-playground/actions/workflows/tests.yml)

[![License](https://img.shields.io/github/license/habedi/rag-playground)](https://github.com/habedi/rag-playground/blob/main/LICENSE)

[![Python version](https://img.shields.io/badge/python-3.10>=-blue)](https://www.python.org/)

This is a playground repository for experimenting and learning about the 

[retrieval augmented generation (RAG)](https://aws.amazon.com/what-is/retrieval-augmented-generation/) paradigm.

Overview of a simple RAG pipeline:



## Poetry

#### Installation

```

pipx install poetry # or uv tool install poetry

```

#### Creating the Environment

```bash

poetry install

```

## Notebooks

| Index | Notebook                                                                                    | Description                                                  |

|-------|---------------------------------------------------------------------------------------------|--------------------------------------------------------------|

| 1     | [embedding_and_indexing_documents](notebooks/basics/embedding_and_indexing_documents.ipynb) | This notebook demonstrates how to embed and index documents. |

## Papers

| Index | Title                                                                           | Authors      | Year | Link                                      |

|-------|---------------------------------------------------------------------------------|--------------|------|-------------------------------------------|

| 1     | Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks                | Lewis et al. | 2020 | [arXiv](https://arxiv.org/abs/2005.11401) |

| 2     | Retrieval-Augmented Generation for Large Language Models: A Survey              | Gao et al.   | 2023 | [arXiv](https://arxiv.org/abs/2312.10997) |

| 3     | A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models | Fan et al.   | 2024 | [arXiv](https://arxiv.org/abs/2405.06211) |

## License

Most files in this repository are licensed under the MIT License---see the [LICENSE](LICENSE) file for details.